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Date: Tue, 3 Nov 2015 15:34:28 +0000 (UTC)
From: "Reynold Xin (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12902491.1444075311000.137819.1446564868204@Atlassian.JIRA>
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Subject: [jira] [Commented] (SPARK-10929) Tungsten fails to acquire memory
 writing to HDFS
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    [ https://issues.apache.org/jira/browse/SPARK-10929?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14987473#comment-14987473 ] 

Reynold Xin commented on SPARK-10929:
-------------------------------------

[~nadenf] it is a pretty big change with risk for regression so unfortunately we can't backport this into branch-1.5.


> Tungsten fails to acquire memory writing to HDFS
> ------------------------------------------------
>
>                 Key: SPARK-10929
>                 URL: https://issues.apache.org/jira/browse/SPARK-10929
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0, 1.5.1
>            Reporter: Naden Franciscus
>            Assignee: Davies Liu
>            Priority: Blocker
>             Fix For: 1.6.0
>
>
> We are executing 20 Spark SQL jobs in parallel using Spark Job Server and hitting the following issue pretty routinely.
> 40GB heap x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> 0.1 with no difference. 
> {code}
> .16): org.apache.spark.SparkException: Task failed while writing rows.
>         at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:250)
>         at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
>         at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
>         at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>         at org.apache.spark.scheduler.Task.run(Task.scala:88)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>         at java.lang.Thread.run(Thread.java:745)
> Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
>         at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
>         at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
>         at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>         at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>         at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50)
>         at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83)
>         at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82)
>         at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
>         at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278)
>         at scala.collection.immutable.List.foreach(List.scala:318)
>         at scala.collection.TraversableLike$class.collect(TraversableLike.scala:278)
>         at scala.collection.AbstractTraversable.collect(Traversable.scala:105)
>         at org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82)
>         at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:97)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> {code}
> I have tried setting spark.buffer.pageSize to both 1MB and 64MB (in spark-defaults.conf) and it makes no difference.
> It also tries to acquire 33554432 bytes of memory in both cases.


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